{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TensorFlow Tutorial #10\n",
    "# Fine-Tuning\n",
    "\n",
    "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
    "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "We have previously seen in Tutorials #08 and #09 how to use a pre-trained Neural Network on a new dataset using so-called Transfer Learning, by re-routing the output of the original model just prior to its classification layers and instead use a new classifier that we had created. Because the original model was 'frozen' its weights could not be further optimized, so whatever had been learned by all the previous layers in the model, could not be fine-tuned to the new data-set.\n",
    "\n",
    "This tutorial shows how to do both Transfer Learning and Fine-Tuning using the Keras API for Tensorflow. We will once again use the Knifey-Spoony dataset introduced in Tutorial #09. We previously used the Inception v3 model but we will use the VGG16 model in this tutorial because its architecture is easier to work with.\n",
    "\n",
    "NOTE: It takes around 15 minutes to execute this Notebook on a laptop PC with a 2.6 GHz CPU and a GTX 1070 GPU. Running it on the CPU alone is estimated to take around 10 hours!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Flowchart\n",
    "\n",
    "The idea is to re-use a pre-trained model, in this case the VGG16 model, which consists of several convolutional layers (actually blocks of multiple convolutional layers), followed by some fully-connected / dense layers and then a softmax output layer for the classification. \n",
    "\n",
    "The dense layers are responsible for combining features from the convolutional layers and this helps in the final classification. So when the VGG16 model is used on another dataset we may have to replace all the dense layers. In this case we add another dense-layer and a dropout-layer to avoid overfitting.\n",
    "\n",
    "The difference between Transfer Learning and Fine-Tuning is that in Transfer Learning we only optimize the weights of the new classification layers we have added, while we keep the weights of the original VGG16 model. In Fine-Tuning we optimize both the weights of the new classification layers we have added, as well as some or all of the layers from the VGG16 model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Flowchart of Transfer Learning & Fine-Tuning](images/10_transfer_learning_flowchart.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import PIL\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the imports from the Keras API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Model, Sequential\n",
    "from tensorflow.keras.layers import Dense, Flatten, Dropout\n",
    "from tensorflow.keras.applications import VGG16\n",
    "from tensorflow.keras.applications.vgg16 import preprocess_input, decode_predictions\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.optimizers import Adam, RMSprop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This was developed using Python 3.6 and TensorFlow version:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.1.0'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper-function for joining a directory and list of filenames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def path_join(dirname, filenames):\n",
    "    return [os.path.join(dirname, filename) for filename in filenames]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper-function for plotting images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function used to plot at most 9 images in a 3x3 grid, and writing the true and predicted classes below each image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_images(images, cls_true, cls_pred=None, smooth=True):\n",
    "\n",
    "    assert len(images) == len(cls_true)\n",
    "\n",
    "    # Create figure with sub-plots.\n",
    "    fig, axes = plt.subplots(3, 3)\n",
    "\n",
    "    # Adjust vertical spacing.\n",
    "    if cls_pred is None:\n",
    "        hspace = 0.3\n",
    "    else:\n",
    "        hspace = 0.6\n",
    "    fig.subplots_adjust(hspace=hspace, wspace=0.3)\n",
    "\n",
    "    # Interpolation type.\n",
    "    if smooth:\n",
    "        interpolation = 'spline16'\n",
    "    else:\n",
    "        interpolation = 'nearest'\n",
    "\n",
    "    for i, ax in enumerate(axes.flat):\n",
    "        # There may be less than 9 images, ensure it doesn't crash.\n",
    "        if i < len(images):\n",
    "            # Plot image.\n",
    "            ax.imshow(images[i],\n",
    "                      interpolation=interpolation)\n",
    "\n",
    "            # Name of the true class.\n",
    "            cls_true_name = class_names[cls_true[i]]\n",
    "\n",
    "            # Show true and predicted classes.\n",
    "            if cls_pred is None:\n",
    "                xlabel = \"True: {0}\".format(cls_true_name)\n",
    "            else:\n",
    "                # Name of the predicted class.\n",
    "                cls_pred_name = class_names[cls_pred[i]]\n",
    "\n",
    "                xlabel = \"True: {0}\\nPred: {1}\".format(cls_true_name, cls_pred_name)\n",
    "\n",
    "            # Show the classes as the label on the x-axis.\n",
    "            ax.set_xlabel(xlabel)\n",
    "        \n",
    "        # Remove ticks from the plot.\n",
    "        ax.set_xticks([])\n",
    "        ax.set_yticks([])\n",
    "    \n",
    "    # Ensure the plot is shown correctly with multiple plots\n",
    "    # in a single Notebook cell.\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper-function for printing confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import a function from sklearn to calculate the confusion-matrix.\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "def print_confusion_matrix(cls_pred):\n",
    "    # cls_pred is an array of the predicted class-number for\n",
    "    # all images in the test-set.\n",
    "\n",
    "    # Get the confusion matrix using sklearn.\n",
    "    cm = confusion_matrix(y_true=cls_test,  # True class for test-set.\n",
    "                          y_pred=cls_pred)  # Predicted class.\n",
    "\n",
    "    print(\"Confusion matrix:\")\n",
    "    \n",
    "    # Print the confusion matrix as text.\n",
    "    print(cm)\n",
    "    \n",
    "    # Print the class-names for easy reference.\n",
    "    for i, class_name in enumerate(class_names):\n",
    "        print(\"({0}) {1}\".format(i, class_name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper-function for plotting example errors\n",
    "\n",
    "Function for plotting examples of images from the test-set that have been mis-classified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_example_errors(cls_pred):\n",
    "    # cls_pred is an array of the predicted class-number for\n",
    "    # all images in the test-set.\n",
    "\n",
    "    # Boolean array whether the predicted class is incorrect.\n",
    "    incorrect = (cls_pred != cls_test)\n",
    "\n",
    "    # Get the file-paths for images that were incorrectly classified.\n",
    "    image_paths = np.array(image_paths_test)[incorrect]\n",
    "\n",
    "    # Load the first 9 images.\n",
    "    images = load_images(image_paths=image_paths[0:9])\n",
    "    \n",
    "    # Get the predicted classes for those images.\n",
    "    cls_pred = cls_pred[incorrect]\n",
    "\n",
    "    # Get the true classes for those images.\n",
    "    cls_true = cls_test[incorrect]\n",
    "    \n",
    "    # Plot the 9 images we have loaded and their corresponding classes.\n",
    "    # We have only loaded 9 images so there is no need to slice those again.\n",
    "    plot_images(images=images,\n",
    "                cls_true=cls_true[0:9],\n",
    "                cls_pred=cls_pred[0:9])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function for calculating the predicted classes of the entire test-set and calling the above function to plot a few examples of mis-classified images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def example_errors():\n",
    "    # The Keras data-generator for the test-set must be reset\n",
    "    # before processing. This is because the generator will loop\n",
    "    # infinitely and keep an internal index into the dataset.\n",
    "    # So it might start in the middle of the test-set if we do\n",
    "    # not reset it first. This makes it impossible to match the\n",
    "    # predicted classes with the input images.\n",
    "    # If we reset the generator, then it always starts at the\n",
    "    # beginning so we know exactly which input-images were used.\n",
    "    generator_test.reset()\n",
    "    \n",
    "    # Predict the classes for all images in the test-set.\n",
    "    y_pred = new_model.predict(generator_test, steps=steps_test)\n",
    "\n",
    "    # Convert the predicted classes from arrays to integers.\n",
    "    cls_pred = np.argmax(y_pred,axis=1)\n",
    "\n",
    "    # Plot examples of mis-classified images.\n",
    "    plot_example_errors(cls_pred)\n",
    "    \n",
    "    # Print the confusion matrix.\n",
    "    print_confusion_matrix(cls_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper-function for loading images\n",
    "\n",
    "The data-set is not loaded into memory, instead it has a list of the files for the images in the training-set and another list of the files for the images in the test-set. This helper-function loads some image-files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_images(image_paths):\n",
    "    # Load the images from disk.\n",
    "    images = [plt.imread(path) for path in image_paths]\n",
    "\n",
    "    # Convert to a numpy array and return it.\n",
    "    return np.asarray(images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper-function for plotting training history\n",
    "\n",
    "This plots the classification accuracy and loss-values recorded during training with the Keras API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_training_history(history):\n",
    "    # Get the classification accuracy and loss-value\n",
    "    # for the training-set.\n",
    "    acc = history.history['categorical_accuracy']\n",
    "    loss = history.history['loss']\n",
    "\n",
    "    # Get it for the validation-set (we only use the test-set).\n",
    "    val_acc = history.history['val_categorical_accuracy']\n",
    "    val_loss = history.history['val_loss']\n",
    "\n",
    "    # Plot the accuracy and loss-values for the training-set.\n",
    "    plt.plot(acc, linestyle='-', color='b', label='Training Acc.')\n",
    "    plt.plot(loss, 'o', color='b', label='Training Loss')\n",
    "    \n",
    "    # Plot it for the test-set.\n",
    "    plt.plot(val_acc, linestyle='--', color='r', label='Test Acc.')\n",
    "    plt.plot(val_loss, 'o', color='r', label='Test Loss')\n",
    "\n",
    "    # Plot title and legend.\n",
    "    plt.title('Training and Test Accuracy')\n",
    "    plt.legend()\n",
    "\n",
    "    # Ensure the plot shows correctly.\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset: Knifey-Spoony\n",
    "\n",
    "The Knifey-Spoony dataset was introduced in Tutorial #09. It was generated from video-files by taking individual frames and converting them to images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import knifey"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download and extract the dataset if it hasn't already been done. It is about 22 MB."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data has apparently already been downloaded and unpacked.\n"
     ]
    }
   ],
   "source": [
    "knifey.maybe_download_and_extract()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset has another directory structure than the Keras API requires, so copy the files into separate directories for the training- and test-sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating dataset from the files in: data/knifey-spoony/\n",
      "- Data loaded from cache-file: data/knifey-spoony/knifey-spoony.pkl\n",
      "- Copied training-set to: data/knifey-spoony/train/\n",
      "- Copied test-set to: data/knifey-spoony/test/\n"
     ]
    }
   ],
   "source": [
    "knifey.copy_files()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The directories where the images are now stored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dir = knifey.train_dir\n",
    "test_dir = knifey.test_dir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-Trained Model: VGG16\n",
    "\n",
    "The following creates an instance of the pre-trained VGG16 model using the Keras API. This automatically downloads the required files if you don't have them already. Note how simple this is in Keras compared to Tutorial #08.\n",
    "\n",
    "The VGG16 model contains a convolutional part and a fully-connected (or dense) part which is used for classification. If `include_top=True` then the whole VGG16 model is downloaded which is about 528 MB. If `include_top=False` then only the convolutional part of the VGG16 model is downloaded which is just 57 MB.\n",
    "\n",
    "We will try and use the pre-trained model for predicting the class of some images in our new dataset, so we have to download the full model, but if you have a slow internet connection, then you can modify the code below to use the smaller pre-trained model without the classification layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model = VGG16(include_top=True, weights='imagenet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Input Pipeline\n",
    "\n",
    "The Keras API has its own way of creating the input pipeline for training a model using files.\n",
    "\n",
    "First we need to know the shape of the tensors expected as input by the pre-trained VGG16 model. In this case it is images of shape 224 x 224 x 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(224, 224)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_shape = model.layers[0].output_shape[0][1:3]\n",
    "input_shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keras uses a so-called data-generator for inputting data into the neural network, which will loop over the data for eternity.\n",
    "\n",
    "We have a small training-set so it helps to artificially inflate its size by making various transformations to the images. We use a built-in data-generator that can make these random transformations. This is also called an augmented dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "datagen_train = ImageDataGenerator(\n",
    "      rescale=1./255,\n",
    "      rotation_range=180,\n",
    "      width_shift_range=0.1,\n",
    "      height_shift_range=0.1,\n",
    "      shear_range=0.1,\n",
    "      zoom_range=[0.9, 1.5],\n",
    "      horizontal_flip=True,\n",
    "      vertical_flip=True,\n",
    "      fill_mode='nearest')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also need a data-generator for the test-set, but this should not do any transformations to the images because we want to know the exact classification accuracy on those specific images. So we just rescale the pixel-values so they are between 0.0 and 1.0 because this is expected by the VGG16 model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "datagen_test = ImageDataGenerator(rescale=1./255)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data-generators will return batches of images. Because the VGG16 model is so large, the batch-size cannot be too large, otherwise you will run out of RAM on the GPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can save the randomly transformed images during training, so as to inspect whether they have been overly distorted, so we have to adjust the parameters for the data-generator above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "if True:\n",
    "    save_to_dir = None\n",
    "else:\n",
    "    save_to_dir='augmented_images/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we create the actual data-generator that will read files from disk, resize the images and return a random batch.\n",
    "\n",
    "It is somewhat awkward that the construction of the data-generator is split into these two steps, but it is probably because there are different kinds of data-generators available for different data-types (images, text, etc.) and sources (memory or disk)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 4170 images belonging to 3 classes.\n"
     ]
    }
   ],
   "source": [
    "generator_train = datagen_train.flow_from_directory(directory=train_dir,\n",
    "                                                    target_size=input_shape,\n",
    "                                                    batch_size=batch_size,\n",
    "                                                    shuffle=True,\n",
    "                                                    save_to_dir=save_to_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data-generator for the test-set should not transform and shuffle the images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 530 images belonging to 3 classes.\n"
     ]
    }
   ],
   "source": [
    "generator_test = datagen_test.flow_from_directory(directory=test_dir,\n",
    "                                                  target_size=input_shape,\n",
    "                                                  batch_size=batch_size,\n",
    "                                                  shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the data-generators will loop for eternity, we need to specify the number of steps to perform during evaluation and prediction on the test-set. Because our test-set contains 530 images and the batch-size is set to 20, the number of steps is 26.5 for one full processing of the test-set. This is why we need to reset the data-generator's counter in the `example_errors()` function above, so it always starts processing from the beginning of the test-set.\n",
    "\n",
    "This is another slightly awkward aspect of the Keras API which could perhaps be improved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26.5"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "steps_test = generator_test.n / batch_size\n",
    "steps_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the file-paths for all the images in the training- and test-sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_paths_train = path_join(train_dir, generator_train.filenames)\n",
    "image_paths_test = path_join(test_dir, generator_test.filenames)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the class-numbers for all the images in the training- and test-sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "cls_train = generator_train.classes\n",
    "cls_test = generator_test.classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the class-names for the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['forky', 'knifey', 'spoony']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_names = list(generator_train.class_indices.keys())\n",
    "class_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the number of classes for the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_classes = generator_train.num_classes\n",
    "num_classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot a few images to see if data is correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the first images from the train-set.\n",
    "images = load_images(image_paths=image_paths_train[0:9])\n",
    "\n",
    "# Get the true classes for those images.\n",
    "cls_true = cls_train[0:9]\n",
    "\n",
    "# Plot the images and labels using our helper-function above.\n",
    "plot_images(images=images, cls_true=cls_true, smooth=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Class Weights\n",
    "\n",
    "The Knifey-Spoony dataset is quite imbalanced because it has few images of forks, more images of knives, and many more images of spoons. This can cause a problem during training because the neural network will be shown many more examples of spoons than forks, so it might become better at recognizing spoons.\n",
    "\n",
    "Here we use scikit-learn to calculate weights that will properly balance the dataset. These weights are applied to the gradient for each image in the batch during training, so as to scale their influence on the overall gradient for the batch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils.class_weight import compute_class_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_weight = compute_class_weight(class_weight='balanced',\n",
    "                                    classes=np.unique(cls_train),\n",
    "                                    y=cls_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note how the weight is about 1.398 for the forky-class and only 0.707 for the spoony-class. This is because there are fewer images for the forky-class so the gradient should be amplified for those images, while the gradient should be lowered for spoony-images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.39839034, 1.14876033, 0.70701933])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['forky', 'knifey', 'spoony']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example Predictions\n",
    "\n",
    "Here we will show a few examples of using the pre-trained VGG16 model for prediction.\n",
    "\n",
    "We need a helper-function for loading and resizing an image so it can be input to the VGG16 model, as well as doing the actual prediction and showing the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(image_path):\n",
    "    # Load and resize the image using PIL.\n",
    "    img = PIL.Image.open(image_path)\n",
    "    img_resized = img.resize(input_shape, PIL.Image.LANCZOS)\n",
    "\n",
    "    # Plot the image.\n",
    "    plt.imshow(img_resized)\n",
    "    plt.show()\n",
    "\n",
    "    # Convert the PIL image to a numpy-array with the proper shape.\n",
    "    img_array = np.expand_dims(np.array(img_resized), axis=0)\n",
    "\n",
    "    # Use the VGG16 model to make a prediction.\n",
    "    # This outputs an array with 1000 numbers corresponding to\n",
    "    # the classes of the ImageNet-dataset.\n",
    "    pred = model.predict(img_array)\n",
    "    \n",
    "    # Decode the output of the VGG16 model.\n",
    "    pred_decoded = decode_predictions(pred)[0]\n",
    "\n",
    "    # Print the predictions.\n",
    "    for code, name, score in pred_decoded:\n",
    "        print(\"{0:>6.2%} : {1}\".format(score, name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then use the VGG16 model on a picture of a parrot which is classified as a macaw (a parrot species) with a fairly high score of 79%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79.02% : macaw\n",
      " 6.61% : bubble\n",
      " 3.64% : vine_snake\n",
      " 1.90% : pinwheel\n",
      " 1.22% : knot\n"
     ]
    }
   ],
   "source": [
    "predict(image_path='images/parrot_cropped1.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then use the VGG16 model to predict the class of one of the images in our new training-set. The VGG16 model is very confused about this image and cannot make a good classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50.31% : shower_curtain\n",
      "17.08% : handkerchief\n",
      "12.75% : mosquito_net\n",
      " 2.87% : window_shade\n",
      " 1.32% : toilet_tissue\n"
     ]
    }
   ],
   "source": [
    "predict(image_path=image_paths_train[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can try it for another image in our new training-set and the VGG16 model is still confused."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "45.08% : shower_curtain\n",
      "21.84% : mosquito_net\n",
      "11.55% : handkerchief\n",
      " 2.02% : window_shade\n",
      " 0.91% : Windsor_tie\n"
     ]
    }
   ],
   "source": [
    "predict(image_path=image_paths_train[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also try an image from our new test-set, and again the VGG16 model is very confused."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26.75% : spoonbill\n",
      " 7.06% : black_stork\n",
      " 7.04% : wooden_spoon\n",
      " 4.21% : limpkin\n",
      " 3.72% : paddle\n"
     ]
    }
   ],
   "source": [
    "predict(image_path=image_paths_test[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transfer Learning\n",
    "\n",
    "The pre-trained VGG16 model was unable to classify images from the Knifey-Spoony dataset. The reason is perhaps that the VGG16 model was trained on the so-called ImageNet dataset which may not have contained many images of cutlery.\n",
    "\n",
    "The lower layers of a Convolutional Neural Network can recognize many different shapes or features in an image. It is the last few fully-connected layers that combine these featuers into classification of a whole image. So we can try and re-route the output of the last convolutional layer of the VGG16 model to a new fully-connected neural network that we create for doing classification on the Knifey-Spoony dataset.\n",
    "\n",
    "First we print a summary of the VGG16 model so we can see the names and types of its layers, as well as the shapes of the tensors flowing between the layers. This is one of the major reasons we are using the VGG16 model in this tutorial, because the Inception v3 model has so many layers that it is confusing when printed out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"vgg16\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 224, 224, 3)]     0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 25088)             0         \n",
      "_________________________________________________________________\n",
      "fc1 (Dense)                  (None, 4096)              102764544 \n",
      "_________________________________________________________________\n",
      "fc2 (Dense)                  (None, 4096)              16781312  \n",
      "_________________________________________________________________\n",
      "predictions (Dense)          (None, 1000)              4097000   \n",
      "=================================================================\n",
      "Total params: 138,357,544\n",
      "Trainable params: 138,357,544\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the last convolutional layer is called 'block5_pool' so we use Keras to get a reference to that layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "transfer_layer = model.get_layer('block5_pool')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We refer to this layer as the Transfer Layer because its output will be re-routed to our new fully-connected neural network which will do the classification for the Knifey-Spoony dataset.\n",
    "\n",
    "The output of the transfer layer has the following shape:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'block5_pool/Identity:0' shape=(None, 7, 7, 512) dtype=float32>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transfer_layer.output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the Keras API it is very simple to create a new model. First we take the part of the VGG16 model from its input-layer to the output of the transfer-layer. We may call this the convolutional model, because it consists of all the convolutional layers from the VGG16 model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_model = Model(inputs=model.input,\n",
    "                   outputs=transfer_layer.output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then use Keras to build a new model on top of this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Start a new Keras Sequential model.\n",
    "new_model = Sequential()\n",
    "\n",
    "# Add the convolutional part of the VGG16 model from above.\n",
    "new_model.add(conv_model)\n",
    "\n",
    "# Flatten the output of the VGG16 model because it is from a\n",
    "# convolutional layer.\n",
    "new_model.add(Flatten())\n",
    "\n",
    "# Add a dense (aka. fully-connected) layer.\n",
    "# This is for combining features that the VGG16 model has\n",
    "# recognized in the image.\n",
    "new_model.add(Dense(1024, activation='relu'))\n",
    "\n",
    "# Add a dropout-layer which may prevent overfitting and\n",
    "# improve generalization ability to unseen data e.g. the test-set.\n",
    "new_model.add(Dropout(0.5))\n",
    "\n",
    "# Add the final layer for the actual classification.\n",
    "new_model.add(Dense(num_classes, activation='softmax'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the Adam optimizer with a fairly low learning-rate. The learning-rate could perhaps be larger. But if you try and train more layers of the original VGG16 model, then the learning-rate should be quite low otherwise the pre-trained weights of the VGG16 model will be distorted and it will be unable to learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = Adam(lr=1e-5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 3 classes in the Knifey-Spoony dataset so Keras needs to use this loss-function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss = 'categorical_crossentropy'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The only performance metric we are interested in is the classification accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics = ['categorical_accuracy']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Helper-function for printing whether a layer in the VGG16 model should be trained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_layer_trainable():\n",
    "    for layer in conv_model.layers:\n",
    "        print(\"{0}:\\t{1}\".format(layer.trainable, layer.name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default all the layers of the VGG16 model are trainable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True:\tinput_1\n",
      "True:\tblock1_conv1\n",
      "True:\tblock1_conv2\n",
      "True:\tblock1_pool\n",
      "True:\tblock2_conv1\n",
      "True:\tblock2_conv2\n",
      "True:\tblock2_pool\n",
      "True:\tblock3_conv1\n",
      "True:\tblock3_conv2\n",
      "True:\tblock3_conv3\n",
      "True:\tblock3_pool\n",
      "True:\tblock4_conv1\n",
      "True:\tblock4_conv2\n",
      "True:\tblock4_conv3\n",
      "True:\tblock4_pool\n",
      "True:\tblock5_conv1\n",
      "True:\tblock5_conv2\n",
      "True:\tblock5_conv3\n",
      "True:\tblock5_pool\n"
     ]
    }
   ],
   "source": [
    "print_layer_trainable()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Transfer Learning we are initially only interested in reusing the pre-trained VGG16 model as it is, so we will disable training for all its layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_model.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "for layer in conv_model.layers:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False:\tinput_1\n",
      "False:\tblock1_conv1\n",
      "False:\tblock1_conv2\n",
      "False:\tblock1_pool\n",
      "False:\tblock2_conv1\n",
      "False:\tblock2_conv2\n",
      "False:\tblock2_pool\n",
      "False:\tblock3_conv1\n",
      "False:\tblock3_conv2\n",
      "False:\tblock3_conv3\n",
      "False:\tblock3_pool\n",
      "False:\tblock4_conv1\n",
      "False:\tblock4_conv2\n",
      "False:\tblock4_conv3\n",
      "False:\tblock4_pool\n",
      "False:\tblock5_conv1\n",
      "False:\tblock5_conv2\n",
      "False:\tblock5_conv3\n",
      "False:\tblock5_pool\n"
     ]
    }
   ],
   "source": [
    "print_layer_trainable()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have changed whether the model's layers are trainable, we need to compile the model for the changes to take effect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_model.compile(optimizer=optimizer, loss=loss, metrics=metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An epoch normally means one full processing of the training-set. But the data-generator that we created above, will produce batches of training-data for eternity. So we need to define the number of steps we want to run for each \"epoch\" and this number gets multiplied by the batch-size defined above. In this case we have 100 steps per epoch and a batch-size of 20, so the \"epoch\" consists of 2000 random images from the training-set. We run 20 such \"epochs\".\n",
    "\n",
    "The reason these particular numbers were chosen, was because they seemed to be sufficient for training with this particular model and dataset, and it didn't take too much time, and resulted in 20 data-points (one for each \"epoch\") which can be plotted afterwards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 20\n",
    "steps_per_epoch = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training the new model is just a single function call in the Keras API. This takes about 6-7 minutes on a GTX 1070 GPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:sample_weight modes were coerced from\n",
      "  ...\n",
      "    to  \n",
      "  ['...']\n",
      "WARNING:tensorflow:sample_weight modes were coerced from\n",
      "  ...\n",
      "    to  \n",
      "  ['...']\n",
      "Train for 100 steps, validate for 26.5 steps\n",
      "Epoch 1/20\n",
      "100/100 [==============================] - 32s 322ms/step - loss: 1.1149 - categorical_accuracy: 0.4585 - val_loss: 0.8945 - val_categorical_accuracy: 0.5604\n",
      "Epoch 2/20\n",
      "100/100 [==============================] - 31s 309ms/step - loss: 0.9385 - categorical_accuracy: 0.5432 - val_loss: 0.7380 - val_categorical_accuracy: 0.7717\n",
      "Epoch 3/20\n",
      "100/100 [==============================] - 29s 285ms/step - loss: 0.8408 - categorical_accuracy: 0.6186 - val_loss: 0.6924 - val_categorical_accuracy: 0.7321\n",
      "Epoch 4/20\n",
      "100/100 [==============================] - 30s 299ms/step - loss: 0.7855 - categorical_accuracy: 0.6432 - val_loss: 0.6994 - val_categorical_accuracy: 0.7057\n",
      "Epoch 5/20\n",
      "100/100 [==============================] - 29s 294ms/step - loss: 0.7128 - categorical_accuracy: 0.6879 - val_loss: 0.7109 - val_categorical_accuracy: 0.6698\n",
      "Epoch 6/20\n",
      "100/100 [==============================] - 29s 286ms/step - loss: 0.6945 - categorical_accuracy: 0.7015 - val_loss: 0.6150 - val_categorical_accuracy: 0.7604\n",
      "Epoch 7/20\n",
      "100/100 [==============================] - 28s 282ms/step - loss: 0.6910 - categorical_accuracy: 0.7060 - val_loss: 0.6316 - val_categorical_accuracy: 0.7321\n",
      "Epoch 8/20\n",
      "100/100 [==============================] - 28s 284ms/step - loss: 0.6269 - categorical_accuracy: 0.7382 - val_loss: 0.5828 - val_categorical_accuracy: 0.7868\n",
      "Epoch 9/20\n",
      "100/100 [==============================] - 30s 300ms/step - loss: 0.6180 - categorical_accuracy: 0.7362 - val_loss: 0.6337 - val_categorical_accuracy: 0.7377\n",
      "Epoch 10/20\n",
      "100/100 [==============================] - 30s 297ms/step - loss: 0.5823 - categorical_accuracy: 0.7568 - val_loss: 0.5569 - val_categorical_accuracy: 0.7868\n",
      "Epoch 11/20\n",
      "100/100 [==============================] - 30s 295ms/step - loss: 0.5969 - categorical_accuracy: 0.7455 - val_loss: 0.6298 - val_categorical_accuracy: 0.7340\n",
      "Epoch 12/20\n",
      "100/100 [==============================] - 29s 289ms/step - loss: 0.5516 - categorical_accuracy: 0.7704 - val_loss: 0.5804 - val_categorical_accuracy: 0.7566\n",
      "Epoch 13/20\n",
      "100/100 [==============================] - 30s 297ms/step - loss: 0.5514 - categorical_accuracy: 0.7770 - val_loss: 0.5879 - val_categorical_accuracy: 0.7453\n",
      "Epoch 14/20\n",
      "100/100 [==============================] - 33s 326ms/step - loss: 0.5239 - categorical_accuracy: 0.7830 - val_loss: 0.5448 - val_categorical_accuracy: 0.7849\n",
      "Epoch 15/20\n",
      "100/100 [==============================] - 32s 325ms/step - loss: 0.5367 - categorical_accuracy: 0.7760 - val_loss: 0.6596 - val_categorical_accuracy: 0.7226\n",
      "Epoch 16/20\n",
      "100/100 [==============================] - 28s 282ms/step - loss: 0.5155 - categorical_accuracy: 0.7860 - val_loss: 0.5385 - val_categorical_accuracy: 0.7755\n",
      "Epoch 17/20\n",
      "100/100 [==============================] - 29s 289ms/step - loss: 0.5058 - categorical_accuracy: 0.7889 - val_loss: 0.6200 - val_categorical_accuracy: 0.7340\n",
      "Epoch 18/20\n",
      "100/100 [==============================] - 28s 283ms/step - loss: 0.4925 - categorical_accuracy: 0.8030 - val_loss: 0.6469 - val_categorical_accuracy: 0.7151\n",
      "Epoch 19/20\n",
      "100/100 [==============================] - 31s 312ms/step - loss: 0.4681 - categorical_accuracy: 0.8145 - val_loss: 0.7350 - val_categorical_accuracy: 0.6906\n",
      "Epoch 20/20\n",
      "100/100 [==============================] - 31s 307ms/step - loss: 0.4743 - categorical_accuracy: 0.8045 - val_loss: 0.5995 - val_categorical_accuracy: 0.7377\n"
     ]
    }
   ],
   "source": [
    "history = new_model.fit(x=generator_train,\n",
    "                        epochs=epochs,\n",
    "                        steps_per_epoch=steps_per_epoch,\n",
    "                        class_weight=class_weight,\n",
    "                        validation_data=generator_test,\n",
    "                        validation_steps=steps_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keras records the performance metrics at the end of each \"epoch\" so they can be plotted later. This shows that the loss-value for the training-set generally decreased during training, but the loss-values for the test-set were a bit more erratic. Similarly, the classification accuracy generally improved on the training-set while it was a bit more erratic on the test-set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After training we can also evaluate the new model's performance on the test-set using a single function call in the Keras API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:sample_weight modes were coerced from\n",
      "  ...\n",
      "    to  \n",
      "  ['...']\n",
      "27/26 [==============================] - 4s 156ms/step - loss: 0.5888 - categorical_accuracy: 0.7377\n"
     ]
    }
   ],
   "source": [
    "result = new_model.evaluate(generator_test, steps=steps_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test-set classification accuracy: 73.77%\n"
     ]
    }
   ],
   "source": [
    "print(\"Test-set classification accuracy: {0:.2%}\".format(result[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot some examples of mis-classified images from the test-set. Some of these images are also difficult for a human to classify.\n",
    "\n",
    "The confusion matrix shows that the new model is especially having problems classifying the forky-class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      "[[137   9   5]\n",
      " [ 56  81   0]\n",
      " [ 56  13 173]]\n",
      "(0) forky\n",
      "(1) knifey\n",
      "(2) spoony\n"
     ]
    }
   ],
   "source": [
    "example_errors()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine-Tuning\n",
    "\n",
    "In Transfer Learning the original pre-trained model is locked or frozen during training of the new classifier. This ensures that the weights of the original VGG16 model will not change. One advantage of this, is that the training of the new classifier will not propagate large gradients back through the VGG16 model that may either distort its weights or cause overfitting to the new dataset.\n",
    "\n",
    "But once the new classifier has been trained we can try and gently fine-tune some of the deeper layers in the VGG16 model as well. We call this Fine-Tuning.\n",
    "\n",
    "It is a bit unclear whether Keras uses the `trainable` boolean in each layer of the original VGG16 model or if it is overrided by the `trainable` boolean in the \"meta-layer\" we call `conv_layer`. So we will enable the `trainable` boolean for both `conv_layer` and all the relevant layers in the original VGG16 model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_model.trainable = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to train the last two convolutional layers whose names contain 'block5' or 'block4'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "for layer in conv_model.layers:\n",
    "    # Boolean whether this layer is trainable.\n",
    "    trainable = ('block5' in layer.name or 'block4' in layer.name)\n",
    "    \n",
    "    # Set the layer's bool.\n",
    "    layer.trainable = trainable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check that this has updated the `trainable` boolean for the relevant layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False:\tinput_1\n",
      "False:\tblock1_conv1\n",
      "False:\tblock1_conv2\n",
      "False:\tblock1_pool\n",
      "False:\tblock2_conv1\n",
      "False:\tblock2_conv2\n",
      "False:\tblock2_pool\n",
      "False:\tblock3_conv1\n",
      "False:\tblock3_conv2\n",
      "False:\tblock3_conv3\n",
      "False:\tblock3_pool\n",
      "True:\tblock4_conv1\n",
      "True:\tblock4_conv2\n",
      "True:\tblock4_conv3\n",
      "True:\tblock4_pool\n",
      "True:\tblock5_conv1\n",
      "True:\tblock5_conv2\n",
      "True:\tblock5_conv3\n",
      "True:\tblock5_pool\n"
     ]
    }
   ],
   "source": [
    "print_layer_trainable()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use a lower learning-rate for the fine-tuning so the weights of the original VGG16 model only get changed slowly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer_fine = Adam(lr=1e-7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because we have defined a new optimizer and have changed the `trainable` boolean for many of the layers in the model, we need to recompile the model so the changes can take effect before we continue training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_model.compile(optimizer=optimizer_fine, loss=loss, metrics=metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training can then be continued so as to fine-tune the VGG16 model along with the new classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:sample_weight modes were coerced from\n",
      "  ...\n",
      "    to  \n",
      "  ['...']\n",
      "WARNING:tensorflow:sample_weight modes were coerced from\n",
      "  ...\n",
      "    to  \n",
      "  ['...']\n",
      "Train for 100 steps, validate for 26.5 steps\n",
      "Epoch 1/20\n",
      "100/100 [==============================] - 34s 339ms/step - loss: 0.4605 - categorical_accuracy: 0.8211 - val_loss: 0.5776 - val_categorical_accuracy: 0.7566\n",
      "Epoch 2/20\n",
      "100/100 [==============================] - 38s 384ms/step - loss: 0.4683 - categorical_accuracy: 0.8175 - val_loss: 0.5600 - val_categorical_accuracy: 0.7604\n",
      "Epoch 3/20\n",
      "100/100 [==============================] - 36s 357ms/step - loss: 0.4643 - categorical_accuracy: 0.8095 - val_loss: 0.5748 - val_categorical_accuracy: 0.7528\n",
      "Epoch 4/20\n",
      "100/100 [==============================] - 37s 371ms/step - loss: 0.4368 - categorical_accuracy: 0.8236 - val_loss: 0.5613 - val_categorical_accuracy: 0.7604\n",
      "Epoch 5/20\n",
      "100/100 [==============================] - 37s 367ms/step - loss: 0.4140 - categorical_accuracy: 0.8317 - val_loss: 0.5490 - val_categorical_accuracy: 0.7642\n",
      "Epoch 6/20\n",
      "100/100 [==============================] - 44s 439ms/step - loss: 0.4456 - categorical_accuracy: 0.8155 - val_loss: 0.5488 - val_categorical_accuracy: 0.7660\n",
      "Epoch 7/20\n",
      "100/100 [==============================] - 45s 454ms/step - loss: 0.4318 - categorical_accuracy: 0.8352 - val_loss: 0.5505 - val_categorical_accuracy: 0.7660\n",
      "Epoch 8/20\n",
      "100/100 [==============================] - 41s 409ms/step - loss: 0.4283 - categorical_accuracy: 0.8265 - val_loss: 0.5580 - val_categorical_accuracy: 0.7604\n",
      "Epoch 9/20\n",
      "100/100 [==============================] - 43s 427ms/step - loss: 0.4197 - categorical_accuracy: 0.8392 - val_loss: 0.5496 - val_categorical_accuracy: 0.7679\n",
      "Epoch 10/20\n",
      "100/100 [==============================] - 43s 431ms/step - loss: 0.4138 - categorical_accuracy: 0.8312 - val_loss: 0.5535 - val_categorical_accuracy: 0.7679\n",
      "Epoch 11/20\n",
      "100/100 [==============================] - 38s 378ms/step - loss: 0.4373 - categorical_accuracy: 0.8332 - val_loss: 0.5449 - val_categorical_accuracy: 0.7679\n",
      "Epoch 12/20\n",
      "100/100 [==============================] - 25s 252ms/step - loss: 0.4046 - categorical_accuracy: 0.8470 - val_loss: 0.5396 - val_categorical_accuracy: 0.7698\n",
      "Epoch 13/20\n",
      "100/100 [==============================] - 23s 234ms/step - loss: 0.4014 - categorical_accuracy: 0.8442 - val_loss: 0.5400 - val_categorical_accuracy: 0.7717\n",
      "Epoch 14/20\n",
      "100/100 [==============================] - 24s 237ms/step - loss: 0.4141 - categorical_accuracy: 0.8365 - val_loss: 0.5473 - val_categorical_accuracy: 0.7679\n",
      "Epoch 15/20\n",
      "100/100 [==============================] - 23s 231ms/step - loss: 0.4117 - categorical_accuracy: 0.8307 - val_loss: 0.5436 - val_categorical_accuracy: 0.7698\n",
      "Epoch 16/20\n",
      "100/100 [==============================] - 23s 229ms/step - loss: 0.3826 - categorical_accuracy: 0.8472 - val_loss: 0.5549 - val_categorical_accuracy: 0.7623\n",
      "Epoch 17/20\n",
      "100/100 [==============================] - 23s 228ms/step - loss: 0.3979 - categorical_accuracy: 0.8442 - val_loss: 0.5402 - val_categorical_accuracy: 0.7698\n",
      "Epoch 18/20\n",
      "100/100 [==============================] - 24s 239ms/step - loss: 0.3941 - categorical_accuracy: 0.8447 - val_loss: 0.5313 - val_categorical_accuracy: 0.7774\n",
      "Epoch 19/20\n",
      "100/100 [==============================] - 24s 244ms/step - loss: 0.3956 - categorical_accuracy: 0.8385 - val_loss: 0.5407 - val_categorical_accuracy: 0.7698\n",
      "Epoch 20/20\n",
      "100/100 [==============================] - 24s 240ms/step - loss: 0.4037 - categorical_accuracy: 0.8281 - val_loss: 0.5352 - val_categorical_accuracy: 0.7755\n"
     ]
    }
   ],
   "source": [
    "history = new_model.fit(x=generator_train,\n",
    "                        epochs=epochs,\n",
    "                        steps_per_epoch=steps_per_epoch,\n",
    "                        class_weight=class_weight,\n",
    "                        validation_data=generator_test,\n",
    "                        validation_steps=steps_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then plot the loss-values and classification accuracy from the training. Depending on the dataset, the original model, the new classifier, and hyper-parameters such as the learning-rate, this may improve the classification accuracies on both training- and test-set, or it may improve on the training-set but worsen it for the test-set in case of overfitting. It may require some experimentation with the parameters to get this right."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training_history(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:sample_weight modes were coerced from\n",
      "  ...\n",
      "    to  \n",
      "  ['...']\n",
      "27/26 [==============================] - 3s 118ms/step - loss: 0.5256 - categorical_accuracy: 0.7755\n"
     ]
    }
   ],
   "source": [
    "result = new_model.evaluate(generator_test, steps=steps_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test-set classification accuracy: 77.55%\n"
     ]
    }
   ],
   "source": [
    "print(\"Test-set classification accuracy: {0:.2%}\".format(result[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot some examples of mis-classified images again, and we can also see from the confusion matrix that the model is still having problems classifying forks correctly.\n",
    "\n",
    "A part of the reason might be that the training-set contains only 994 images of forks, while it contains 1210 images of knives and 1966 images of spoons. Even though we have weighted the classes to compensate for this imbalance, and we have also augmented the training-set by randomly transforming the images in different ways during training, it may not be enough for the model to properly learn to recognize forks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      "[[133   7  11]\n",
      " [ 48  88   1]\n",
      " [ 40  12 190]]\n",
      "(0) forky\n",
      "(1) knifey\n",
      "(2) spoony\n"
     ]
    }
   ],
   "source": [
    "example_errors()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "This tutorial showed how to use the Keras API for TensorFlow to do both Transfer Learning and Fine-Tuning of the pre-trained VGG16 model on a new dataset. It is much easier to implement this using the Keras API rather than directly in TensorFlow.\n",
    "\n",
    "Whether Fine-Tuning improves the classification accuracy over just using Transfer Learning depends on the pre-trained model, the transfer-layer you choose, your dataset, and how you train the new model. You may experience improved performance from the fine-tuning, or you may experience worse performance if the fine-tuned model is overfitting your training-data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "\n",
    "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n",
    "\n",
    "You may want to backup this Notebook and the other files before making any changes.\n",
    "\n",
    "* Try using other layers in the VGG16 model as the transfer layer. How does it affect the training and classification accuracy?\n",
    "* Change the new classification layers we added. Can you improve the classification accuracy by either increasing or decreasing the number of nodes in the fully-connected / dense layer?\n",
    "* What happens if you remove the Dropout-layer in the new classifier?\n",
    "* Change the learning-rates for both Transfer Learning and Fine-Tuning.\n",
    "* Try fine-tuning on the whole VGG16 model instead of just the last few layers. How does it affect the classification accuracy on the training- and test-sets? Why?\n",
    "* Try doing the fine-tuning from the beginning so the new classification layers are trained from scratch along with all the convolutional layers of the VGG16 model. You may need to lower the learning-rate for the optimizer.\n",
    "* Add a few images from the test-set to the training-set. Does that improve performance?\n",
    "* Try deleting some of the knifey and spoony images from the training-set so the classes all have the same number of images. Does that improve the numbers in the confusion-matrix?\n",
    "* Use another dataset.\n",
    "* Use another pre-trained model available from Keras.\n",
    "* Explain to a friend how the program works."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## License (MIT)\n",
    "\n",
    "Copyright (c) 2016-2017 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
    "\n",
    "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
    "\n",
    "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
    "\n",
    "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
   ]
  }
 ],
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